Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Sample Size”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 595 records · Page 33Linked to original sources
PubMed1988

Estimation of sample sizes in case-control studies with multiple controls per case: dichotomous data.

In planning case-control studies with matched sets, the calculation of exact sample sizes is difficult, because this calculation depends on some nuisance parameters that are usually unknown in practice. Using the Pitman efficiency of Miettinen's test relative to McNemar's test, Schlesselman and Stolley (Case-control studies: design, conduct, analysis. Oxford: Oxford University Press, 1982:144-70) derived an approximate sample size formula which requires the assumption that the difference in exposure rates between cases and controls is small. Furthermore, on the basis of an assumption similar to that used in Schlesselman and Stolley's approach, Taylor (Stat Med 1986;5:29-36) proposed another approximation formula. In this paper, an alternative and explicit formula that does not require the exposure difference to be small between case and control groups has been derived. Monte Carlo studies are given for comparing the accuracy of these three procedures. The results indicate that when odds ratios of exposure between cases and controls are small (less than or equal to 4) and there is more than one matched control per case, the formula derived in this paper seems to be the best. When odds ratios are large (greater than or equal to 5), however, Taylor's more conservative estimate is recommended, unless the exposure prevalence in the general population is large (0.9).

Epidemiologic Methods↗
PubMed2007

Evidence-based sample size calculations based upon updated meta-analysis.

Meta-analyses of randomized controlled trials (RCTs) provide the highest level of evidence regarding the effectiveness of interventions and as such underpin much of evidence-based medicine. Despite this, meta-analyses are usually produced as observational by-products of the existing literature, with no formal consideration of future meta-analyses when individual trials are being designed. Basing the sample size of a new trial on the results of an updated meta-analysis which will include it, may sometimes make more sense than powering the trial in isolation. A framework for sample size calculation for a future RCT based on the results of a meta-analysis of the existing evidence is presented. Both fixed and random effect approaches are explored through an example. Bayesian Markov Chain Monte Carlo simulation modelling is used for the random effects model since it has computational advantages over the classical approach. Several criteria on which to base inference and hence power are considered. The prior expectation of the power is averaged over the prior distribution for the unknown true treatment effect. An extension to the framework allowing for consideration of the design for a series of new trials is also presented. Results suggest that power can be highly dependent on the statistical model used to meta-analyse the data and even very large studies may have little impact on a meta-analysis when there is considerable between study heterogeneity. This raises issues regarding the appropriateness of the use of random effect models when designing and drawing inferences across a series of studies.

Anti-Bacterial Agents↗
PubMed2001

Sample size requirements to control for stochastic variation in magnitude and location of allele-sharing linkage statistics in affected sibling pairs.

Typically, genome scans for complex disease have produced linkage peaks which have proved difficult to replicate in additional independent studies. Here we confirm that this may be due to the large variance in magnitude and position of the linkage statistics when maximized across a region. Simulations suggest that for genes of moderate effect (locus-specific sibling relative risks lambda s in the range 1.23-1.39), sample sizes of less than 500 affected sib pairs will give unacceptably large standard errors in the magnitudes and locations of significant linkage results. For genes of small effect (lambda s < or = 1.13), sample sizes in the region of 1000-2000 pairs may be required to achieve consistency of results between different studies. These figures have important implications for our confidence in location estimates for disease genes obtained from linkage studies of modest size. In particular, collection of larger data sets and/or analysis strategies such as conditioning or narrowing the phenotype definition, in order to increase the relative effect size, may be required before embarking on positional cloning.

Alleles↗
PubMed1988

Minimal herd sample size for determination of blood copper status of cattle.

Copper is required by cattle for synthesis of numerous proteins and enzymes. Copper deficiency in cattle results in a variety of signs ranging from weight loss to diarrhea. In the fall of 1984 and 1985, blood samples were collected from 22 cattle herds near Gunnison, Colo. Approximately one third of the herds were classified as copper deficient (ie, mean serum copper concentration less than 0.6 mg/L). The inherent variability of serum copper concentrations within a herd mandates the determination of the minimal number of cattle to be tested to properly assess the blood copper status of a herd. Coefficients of variation for serum copper concentration were used to calculate a minimal sample size, with a 95% confidence interval for each herd. Minimal sample size ranged from 3 to 55 cattle/herd (ie, 1 to 22% of the herd); this finding suggested that the usual procedure of testing 10% of the herd may be inappropriate.

Animals↗
PubMed1982

Sample size requirements for comparing time-to-failure among k treatment groups.

Clinical trials are commonly undertaken to compare three or more treatment groups. In this paper sample size requirements are provided for the k group comparative clinical trial in which time-to-failure is the measure of treatment efficacy. Time-to-failure is assumed to have an exponential distribution and so these results generalize those of George and Desu. When the alternative hypothesis specifies only the magnitude of the largest difference among the treatment groups, a particularly simple expression is obtained. It is shown that heuristic use of sample size formulae or tables for comparing two treatment groups is not adequate for obtaining sufficient power and properly accounting for the multiple comparisons possible with k greater than or equal to 3 treatment groups.

Clinical Trials as Topic↗
PubMed2005

Sample size for a two-group comparison of repeated binary measurements using GEE.

Controlled clinical trials often randomize subjects to two treatment groups and repeatedly evaluate them at baseline and intervals across a treatment period of fixed duration. A popular primary objective in these trials is to compare the change rates in the repeated measurements between treatment groups. Repeated measurements usually involve missing data and a serial correlation within each subject. The generalized estimating equation (GEE) method has been widely used to fit the time trend in repeated measurements because of its robustness to random missing and mispecification of the true correlation structure. In this paper, we propose a closed form sample size formula for comparing the change rates of binary repeated measurements using GEE for a two-group comparison. The sample size formula is derived incorporating missing patterns, such as independent missing and monotone missing, and correlation structures, such as AR(1) model. We also propose an algorithm to generate correlated binary data with arbitrary marginal means and a Markov dependency and use it in simulation studies.

Algorithms↗
PubMed2003

Experimental design and sample size determination for testing synergism in drug combination studies based on uniform measures.

In anticancer drug development, the combined use of two drugs is an important strategy to achieve greater therapeutic success. Often combination studies are performed in animal (mostly mice) models before clinical trials are conducted. These experiments on mice are costly, especially with combination studies. However, experimental designs and sample size derivations for the joint action of drugs are not currently available except for a few cases where strong model assumptions are made. For example, Abdelbasit and Plackett proposed an optimal design assuming that the dose-response relationship follows some specified linear models. Tallarida et al. derived a design by fixing the mixture ratio and used a t-test to detect the simple similar action. The issue is that in reality we usually do not have enough information on the joint action of the two compounds before experiment and to understand their joint action is exactly our study goal. In this paper, we first propose a novel non-parametric model that does not impose such strong assumptions on the joint action. We then propose an experimental design for the joint action using uniform measure in this non-parametric model. This design is optimal in the sense that it reduces the variability in modelling synergy while allocating the doses to minimize the number of experimental units and to extract maximum information on the joint action of the compounds. Based on this design, we propose a robust F-test to detect departures from the simple similar action of two compounds and a method to determine sample sizes that are economically feasible. We illustrate the method with a study of the joint action of two new anticancer agents: temozolomide and irinotecan.

Animals↗
PubMed2000

Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease: power calculations and estimates of sample size to detect treatment effects.

OBJECTIVE: To evaluate the rate of brain atrophy calculated from serial magnetic resonance imaging (MRI) registration as a surrogate marker of disease progression for use in clinical trials in Alzheimer disease (AD). METHODS: Eighteen patients with mild to moderate AD and 18 age-matched normal controls underwent 2 MRI brain scans separated by a 12-month interval. Each individual's later scan was registered to their first scan, and the volume of cerebral tissue loss calculated directly from the registered and subtracted MRI scan pairs. The mean and SD of the rate of brain volume changes were used to estimate the sample sizes that would be needed in a clinical trial with a drug anticipated to modify disease progression by varying degrees. Comparable sample size estimates were performed with data for other methods of monitoring rates of brain atrophy, extracted from published papers. RESULTS: The mean (SD) rate of brain atrophy for the patients with AD was 2.37% (1.11%) per year, while in the control group it was 0.41% (0.47%) per year. Based on these figures, to have 90% power to detect a drug effect equivalent to a 20% reduction in the rate of atrophy, 207 patients would be needed in each treatment arm. This assumes a 1-year placebo-controlled trial with a 10% patient dropout rate, and that 10% of scan pairs are unusable. CONCLUSION: Registration of serial MRI volume images provides a powerful method of quantification of brain atrophy that can be used to monitor progression of AD in clinical trials.

Aged↗
PubMed1987

A sample size program for "proving the null hypothesis".

A flexible APL computer program is presented for assistance in planning clinical trials designed to test the equivalence of two therapies. It is a substitute for the sample size graphs of Blackwelder and Chang and provides the option to calculate any of the six characteristics--sample size, significance level, power, success probability of standard therapy, success probability of experimental therapy, and minimum difference of interest--if five of them are prespecified.

Clinical Trials as Topic↗
PubMed2000

Variance and sample size calculations in quality-of-life--adjusted survival analysis (Q-TWiST).

The Quality-Adjusted Time Without Symptoms or Toxicity (Q-TWiST) statistic previously introduced by Glasziou, Simes and Gelber (1990, Statistics in Medicine 9, 1259-1276) combines toxicity, disease-free survival, and overall survival information in assessing the impact of treatments on the lives of patients. This methodology has received positive reviews from clinicians as intuitive and useful, but to date, the variance of this statistic has remained unspecified. We review aspects of the Q-TWiST method for analyzing clinical trial data, extend the method to accommodate multiple treatment arms, and provide closed-form asymptotic variance formulas. We also provide a framework for designing Q-TWiST clinical trials with sample sizes determined using the derived asymptotic variance formulas. Trials currently collecting quality of life data did not have the benefit of these sample size calculation techniques in designing their studies.

Biometry↗
PubMed1999

Power and sample size for testing homogeneity of relative risks in prospective studies.

Power and sample-size formulas for testing the homogeneity of relative risks using the score method are presented. The homogeneity score test (Gart, 1985, Biometrika 72, 673-677) is formally equivalent to the Pearson chi-square test, although they look different. Results of this paper may be useful in assessing the validity of the model of a common relative risk before combining several 2 x 2 tables or in designing a prospective study for detecting heterogeneity of relative risks.

Animals↗
PubMed2002

Sample size considerations for studies of intervention efficacy in the occupational setting.

OBJECTIVE: Due to a shared environment and similarities among workers within a worksite, the strongest analytical design to evaluate the efficacy of an intervention to reduce occupational health or safety hazards is to randomly assign worksites, not workers, to the intervention and comparison conditions. Statistical methods are well described for estimating the sample size when the unit of assignment is a group but these methods have not been applied in the evaluation of occupational health and safety interventions. We review and apply the statistical methods for group-randomized trials in planning a study to evaluate the effectiveness of technical/behavioral interventions to reduce wood dust levels among small woodworking businesses. METHODS: We conducted a pilot study in five small woodworking businesses to estimate variance components between and within worksites and between and within workers. In each worksite, 8 h time-weighted dust concentrations were obtained for each production employee on between two and five occasions. With these data, we estimated the parameters necessary to calculate the percent change in dust concentrations that we could detect (alpha = 0.05, power = 80%) for a range of worksites per condition, workers per worksite and repeat measurements per worker. RESULTS: The mean wood dust concentration across woodworking businesses was 4.53 mg/m3. The measure of similarity among workers within a woodworking business was large (intraclass correlation = 0.5086). Repeated measurements within a worker were weakly correlated (r = 0.1927) while repeated measurements within a worksite were strongly correlated (r = 0.8925). The dominant factor in the sample size calculation was the number of worksites per condition, with the number of workers per worksite playing a lesser role. We also observed that increasing the number of repeat measurements per person had little benefit given the low within-worker correlation in our data. We found that 30 worksites per condition and 10 workers per worksite would give us 80% power to detect a reduction of approximately 30% in wood dust levels (alpha = 0.05). CONCLUSIONS: Our results demonstrate the application of the group-randomized trials methodology to evaluate interventions to reduce occupational hazards. The methodology is widely applicable and not limited to the context of wood dust reduction.

Dust↗
PubMed2005

On the sample size requirement in genetic association tests when the proportion of false positives is controlled.

With respect to the multiple-tests problem, recently an increasing amount of attention has been paid to control the false discovery rate (FDR), the positive false discovery rate (pFDR), and the proportion of false positives (PFP). The new approaches are generally believed to be more powerful than the classical Bonferroni one. This article focuses on the PFP approach. It demonstrates via examples in genetic association studies that the Bonferroni procedure can be more powerful than the PFP-control one and also shows the intrinsic connection between controlling the PFP and controlling the overall type I error rate. Since controlling the PFP does not necessarily lead to a desired power level, this article addresses the design issue and recommends the sample sizes that can attain the desired power levels when the PFP is controlled. The results in this article also provide rough guidance for the sample sizes to achieve the desired power levels when the FDR and especially the pFDR are controlled.

Algorithms↗
PubMed2000

Sample size calculations for risk equivalence testing in pharmacoepidemiology.

Equivalence testing has been widely discussed and is commonly used in pharmacokinetics (bioequivalence) and clinical trials (therapeutic equivalence). It can also be applied to pharmacoepidemiology, where the aim may be to test with a known risk (one-group design) or with another drug (two-group design). Whether the approach is two-sided or one-sided, predefined equivalence limits are required. The definition of the equivalence region can be based on either risk difference or risk ratio. Risk equivalence testing is complicated by the binary nature of the outcome, its low frequency, and by the absence of commonly defined equivalence limits for differences or ratios. In this context, we consider usable formulae for sample sizes. In most cases, at least when the risk studied is large enough (above 1/1,000), it appears that these formulae result in sample sizes that may be acceptable for practical purposes. For example, demonstrating equivalence with a known risk of 0.01, a 20% maximal risk difference, and a one-sided test (alpha = 0.05 and beta = 0.2) requires: under the one-group design (known risk), 15,309 patients; and under the two-group design, 30,617 patients per group. This approach is the appropriate way to conclude equivalence, rather than the commonly used approach of difference testing and concluding equivalence when the null hypothesis of equality is not rejected.

Clinical Trials as Topic↗
PubMed2006

Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs.

BACKGROUND: Although statistical methodology is well developed for comparing diagnostic tests in terms of their sensitivities and specificities, comparative inference about predictive values is not. PURPOSE: In this paper we consider the design and analysis of studies comparing the positive and negative predictive values of two diagnostic tests that are measured on all subjects. METHODS: We focus on comparing tests using the relative positive and negative predictive values. We discuss directly estimating these quantities from the data and derive analytic variance expressions. Sample size formulas for study design ensue. RESULTS: We analyze data on patients with cystic fibrosis to illustrate the methodology. This approach is compared and contrasted with an existing regression framework that can also be used for similar analysis purposes and yields similar results. CONCLUSIONS: We have developed a new approach for comparing the predictive values of two tests that gives rise to sample size formulas for study design.

Biometry↗
PubMed2002

Detecting dose-response using contrasts: asymptotic power and sample size determination for binomial data.

Recently, Stewart and Ruberg proposed the use of contrast tests for detecting dose-response relationships. They considered in particular bivariate contrasts for healing rates and gave several possibilities of defining adequate sets of coefficients. This paper extends their work in several directions. First, asymptotic power expressions for both single and multiple contrast tests are derived. Secondly, well known trend tests are rewritten as multiple contrast tests, thus alleviating the inherent problem of choosing adequate contrast coefficients. Thirdly, recent results on the efficient calculation of multivariate normal probabilities overcome the traditional simulation-based methods for the numerical computations. Modifications of the power formulae allow the calculation of sample sizes for given type I and II errors, the spontaneous rate, and the dose-response shape. Some numerical results of a power study for small to moderate sample sizes show that the nominal power is a reasonably good approximation to the actual power. An example from a clinical trial illustrates the practical use of the results.

Cabergoline↗
PubMed1989

[The power and sample size of F tests of variance proportions in multiple regression].

We would consider not only the significance level alpha, but also the power 1-beta, when making a significance test. This paper presents the method of finding the power of the significance test of a multiple regression equation, and the method of finding the necessary sample size, and has listed the power table and the sample size table for ready reference.

Humans↗
PubMed1997

Sample size calculations for studies with correlated observations.

Correlated data occur frequently in biomedical research. Examples include longitudinal studies, family studies, and ophthalmologic studies. In this paper, we present a method to compute sample sizes and statistical powers for studies involving correlated observations. This is a multivariate extension of the work by Self and Mauritsen (1988, Biometrics 44, 79-86), who derived a sample size and power formula for generalized linear models based on the score statistic. For correlated data, we appeal to a statistic based on the generalized estimating equation method (Liang and Zeger, 1986, Biometrika 73, 13-22). We highlight the additional assumptions needed to deal with correlated data. Some special cases that are commonly seen in practice are discussed, followed by simulation studies.

Biometry↗